170 results about "Event graph" patented technology

The invention relates to the technical field of engineering construction supervision, and discloses a building engineering construction supervision system based on big data analysis, and the system comprises a multi-modal data collection module which is used for collecting multi-source data of a construction site, and the multi-source data comprises structure sensor data, environment monitoring data, video image data, construction log data and building information model (BIM) state data; performing standardization processing and time synchronization on the data to generate a construction state data sequence; and the construction event modeling module identifies key events in the construction process based on the construction state data sequence and constructs a construction event graph, and the construction event graph is composed of event nodes representing construction events and event edges representing event collaboration or time correlation. By introducing an event atlas construction mechanism based on multi-source construction data driving, structured expression and semantic association mapping of key behavior units of a construction site are realized, and the problem of insufficient non-structured information processing capability in construction monitoring is overcome.

Systems, apparatuses, and methods for root cause analysis of a computing network are disclosed. A network management system builds a causation model based on causal mappings corresponding to network events. The causal mapping identifies a logical order of occurrence between a given network event and other network events. Network event obtained from the computing network is analyzed using the causation model for performing a root cause analysis for the computing network by generating a network event graph defining a one-to-one relationship between a given network event and one or more other network events. The causation model is built using determined hierarchical relationship of network events with a plurality of network entities as connected within the computing network.

The invention discloses a multi-modal power grid fault diagnosis method and system based on a causal event atlas, and belongs to the technical field of intelligent operation and maintenance of power systems. The method comprises the following steps: preprocessing historical fault case data of power grid equipment, and constructing a causal event atlas database; when a fault diagnosis request is received, analyzing the fault diagnosis request by the planning agent, generating an initial fault hypothesis set in combination with power field knowledge, endowing a corresponding credibility score to the fault hypothesis, retrieving the cause subgraph as evidence in an iterative loop, and updating the credibility score of the fault hypothesis by the reasoning agent; and generating a multi-modal diagnosis report after the termination condition is met. According to the method, the problems of insufficient causal modeling and poor interpretability of a traditional method are solved, and the diagnosis accuracy, efficiency and user credibility are remarkably improved.

The invention relates to the technical field of information, and provides a bulk commodity transaction system based on a block chain, and the system comprises a parameter and publishing module which uniformly receives and stores parameters; the unified examination and approval engine performs isomorphic arrangement and gray level updating through a configurable template and a state machine; consistency snapshots and recovery are paused / abnormally collected snapshots and monotonically replayed, and it is guaranteed that the sequence is consistent with a window; the security fund risk control uses DSL + fund DAG to drive freezing / unfreezing / deduction and account checking; and block chain evidence storage and auditing execute field-level hash and pedigree anchoring to realize verifiable traceability. According to the system, an end-to-end consistency and verifiability framework is constructed around the aspects of field-level fingerprinting, unified examination and approval, windowed stable sorting, consistency snapshot and monotone replay, fund event patterning, contract assembling and signature anchoring, settlement / final recalculation and pedigree uplink and offline verification; and under the conditions of abnormity, recovery and version evolution, each link keeps uniform caliber, stable sorting and approvable state.

An attack detection and source tracing method includes acquiring entities in a target network environment and interaction event information between the entities and constructing a network event graph with the entities and the interaction event information; determining a graph embedding vector of each interaction event information in the network event graph as feature information based on a preset attack feature recognition model and determining an attack event in the network event graph according to the feature information; determining dependencies between the attack event and remaining interaction event information in the network event graph and searching for a corresponding interaction event information as source tracing information of the attack event according to the dependencies.

The invention relates to a computer vision technology, in particular to an event camera target detection method based on Euclidean-hyperbolic motion perception, which comprises the following steps: down-sampling original event data, constructing an event graph by using the down-sampled data, and inputting the event graph into a double-space network for target detection; performing local perception feature extraction on the event graph by using a graph convolutional network based on a B-spline kernel function, and projecting the extracted features to a hyperbolic space; extracting global event dependency features from the local sensing features mapped to the hyperbolic space through a learnable curvature hyperbolic graph convolutional network; and decoding the global event dependency feature and inputting the decoded global event dependency feature into a sensing head, and outputting a target detection result by the sensing head. The method solves the problems of noise suppression, topological mapping and hierarchical expression in event data, has good application prospects and popularization value, and is particularly suitable for real-time target sensing tasks in complex dynamic environments such as automatic driving and robot navigation.

The invention relates to the related technical field of computer vision, in particular to an event-guided image motion deblurring method in a real scene, which comprises the following steps of: 1, constructing an event-image two-branch fusion deblurring network, and obtaining effective information in a blurred image and an event stream to realize motion blurred image restoration; and 2, in the event branch, converting a spatially sparse event stream into a dense three-dimensional tensor form by using a voxel grid event representation method, and obtaining a spatio-temporal motion feature representation of the event through a spatio-temporal motion enhancement module. According to the event-guided image motion deblurring method in the real scene, an event stream in exposure time is divided into N time slices, the N time slices are respectively accumulated into 2xHxW event frames according to polarities, a 2NxHxW three-dimensional event tensor is constructed, time sequence dependency in 2N event channels is modeled by adopting a bidirectional channel scanning strategy based on a selection state space model, and the time sequence dependency in the 2N event channels is calculated. Information of motion over time over the exposure time is modeled.

The invention discloses a laboratory risk control method based on a graph neural network, and the method comprises the following steps: S1, constructing a dynamically updated interaction event graph based on an interaction event sequence of laboratory equipment, personnel and environment; s2, dynamically updating the state space of each node according to the event preorder relation, and generating a synchronous node state sequence; s3, generating an early warning feature set of a node state change trend by adopting a graph convolution operation; s4, constructing an inter-node risk incidence matrix according to the early warning feature set, and generating an updated risk propagation path; s5, evaluating the probability of transition from the node state to the abnormal state, and generating a state risk prediction value; and S6, inputting a wolf pack optimization algorithm, and optimizing a parameter combination to obtain a laboratory risk prediction result. According to the invention, the real-time performance and accuracy of laboratory risk prediction are improved.

The invention discloses a power grid dispatching report generation method based on natural language processing, relates to the technical field of power grid dispatching report processing, and aims at solving the problems that a traditional report generation mode is low in efficiency and large in subjective deviation. The method comprises the steps that multi-source data are integrated, the data are preprocessed, and multi-dimensional time sequence data are output; generating an event atlas based on the high-dimensional data abstract and the state recognition result; converting the structured data into basic natural language expression, and filling dynamic content; generating a report text; generating a dynamic visual chart and an image-text report based on the generated report text, the event graph and trend data generated by the time sequence analysis model; an image-text report is adopted, the report is divided into key suggestions and detail descriptions, and highlighted high-risk event prompts are generated. According to the method, the report generation efficiency can be improved, subjective deviation in manual analysis is eliminated, and objectivity and specialty of report content are ensured.

The invention belongs to the field of network security, particularly relates to a network security event analysis method, system and equipment, and aims to solve the problem that the existing network security event analysis and detection technology is poor in detection effect. Comprising the following steps: determining common data and suspicious data in a network security data set; constructing a multi-dimensional base line for the suspicious data, and generating a composite feature vector in combination with the feature vector of the common data; identifying an abnormal line in the composite feature vector as an abnormal event node; constructing an event graph based on the relationship between different target devices, the relationship between the target device and the user, and the relationship between the target device and the abnormal event node; determining an event type based on a node feature matrix of the event graph; and generating a network security event analysis result based on the event type, the abnormal score, the event occurrence time, the event occurrence position and the attack path. The network security event analysis method and the network security event analysis device have stronger generalization ability for novel attacks, and improve the accuracy of network security event analysis.

The invention discloses a field event element extraction method and system based on prompt learning and knowledge completion, and the method comprises the steps: converting an extraction task into a constrained generation problem through structured prompt, achieving the stable extraction under a small number of labeled samples through the existing knowledge prior of a large language model, reducing the dependence on large-scale labeled data, and improving the extraction efficiency. And the cross-domain adaptive capacity is improved. A vectorization knowledge retrieval mechanism is introduced, related evidences are obtained from an external knowledge source, elements which are not clearly expressed in a text are complemented, the integrity of information is enhanced, and the accuracy and credibility of a result are improved through knowledge verification. And through a self-adaptive fusion mechanism of prompt and knowledge, the model can flexibly balance text context and external knowledge, and the robustness of complex context and fuzzy expression is improved. And finally, standardized and structured element data are output, a convenient interface is provided for downstream event atlas construction and analysis tasks, and the automation degree of domain knowledge processing and the system integration efficiency are improved.

The invention discloses a smart city event perception and emergency scheduling method based on artificial intelligence. The method comprises the following steps: S1, constructing a standardized structured sample; s2, forming a city sub-event graph; s3, constructing a Bayesian event inference network, and synchronously recording intermediate layer feature response; s4, outputting a dynamic thermodynamic diagram as an event perception model; s5, constructing a multi-target scheduling graph, and generating an emergency scheduling task; s6, taking training of a lightweight student model as a target, constructing a fusion type knowledge distillation mechanism for edge deployment, and fusing teacher model output, intermediate layer feature response and Bayesian causal structure consistency as a distillation target; and S7, adjusting an event state association structure in the Bayesian event inference network and a response weight distribution mechanism of the lightweight student model. The method has the advantages of high reasoning precision, low response delay and edge deployment.

The invention provides a monocular depth estimation method and product based on convolution compensation dual-channel self-attention, and relates to the field of computer vision. Comprising the following steps: converting an event flow of a target scene into three-dimensional tensor representation; obtaining event image fusion multi-scale spatial features based on the image of the target scene and the three-dimensional tensor representation; modeling spatial context correlation in a spatial dimension by utilizing event image fusion multi-scale spatial features through a context modeling self-attention branch to obtain a context modeling self-attention result; through a modal fusion self-attention branch, using the event image to fuse the modal correlation of the multi-scale spatial features in the channel dimension modeling image and the event, and obtaining a modal fusion self-attention result; and pixel-level depth prediction is carried out by using a context modeling self-attention result and a modal fusion self-attention result to obtain a depth map, so that complementary characteristics between an event and an image are fully mined, fine-grained depth fusion expression is realized, and depth estimation precision and generalization ability are effectively improved.

The invention discloses a risk level assessment method based on a graph neural network, and belongs to the technical field of artificial intelligence and machine learning. The method comprises the following steps: firstly, detecting a plurality of events, evaluating risk levels, and cleaning data to construct a risk data set; secondly, based on a feature extractor, extracting features of the event nodes and the index nodes as high-dimensional vectors, and constructing an event graph by taking months as units; and then constructing a graph convolutional neural network, performing training based on a graph data set to obtain a risk level evaluation model based on a month event graph, and finally performing risk level evaluation by using the trained model. According to the method, risk level prediction is completed by applying the graph neural network technology, the expressive force and the operation efficiency of the model can be improved by adopting the combination of the GCN architecture and the readout layer, and the learning ability of the model is also enhanced.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a video event association reasoning method based on a knowledge graph. The method comprises the following steps: S1, collecting video data and preprocessing the video data; s2, constructing an event graph according to a preprocessing result, and generating a state representation sequence through a graph convolutional network; s3, constructing a DreamerV3 world model, inputting a state representation sequence, and generating an event trajectory set; s4, performing trajectory value evaluation operation on the event trajectory set, and constructing a target path based on an evaluation result; s5, executing a structure alignment operation in the event graph according to the target path, and generating a reasoning knowledge graph; and S6, based on the inference knowledge graph, outputting a video event association result according to a time sequence. According to the method, video identification, map modeling and trajectory prediction methods are fully fused, and an efficient closed loop of key event extraction and map-level causal reasoning is realized.

An attack detection and traceback method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring entities in a target network environment and interaction event information between the entities, and constructing a network event graph from the entities and the interaction event information; on the basis of a preset attack feature recognition model, determining graph embedding vectors of each piece of interaction event information in the network event graph as feature information, and on the basis of the feature information, determining an attack event in the network event graph; and determining the degree of dependency between the attack event and remaining interaction event information in the network event graph, and on the basis of the degree of dependency, searching for corresponding interaction event information as traceback information for the attack event.

The invention discloses a digital work card-driven business window multi-source recording data fusion intelligent analysis system, and particularly relates to the technical field of voice processing. The system is based on asynchronous audio streams collected by a wearable digital work card, an array microphone and environment pickup equipment; structured alignment and unified time reference construction of multi-source recording data are realized by adopting the steps of anchor point detection, event graph construction, cross-source matching, elastic time distortion alignment and the like. The system completes cross-source event marking by introducing multiple types of anchor point detectors (semantic keywords, acoustic abrupt changes and prompt tones); constructing an event alignment graph and executing confidence classification in combination with a unified clipping and cross sliding matching strategy in the anchor point pairing process; and then performing multi-scale alignment and resampling on the audio stream based on a layered elastic time warping method, and finally outputting a frame-level synchronous voice data stream to provide unified time support for downstream speaker separation, behavior auditing and semantic mapping.

The invention discloses an enterprise knowledge base content retrieval method and device based on an event graph, equipment and a storage medium, and relates to the technical field of natural language process.The method comprises the steps that candidate event information is extracted from an initial enterprise knowledge base text to obtain a structured event sentence set, all the candidate event information in the event sentence set is set as nodes, and the nodes are stored in the enterprise knowledge base text; setting a time sequence relationship among the candidate event information as an edge to obtain an event graph; analyzing user query content to obtain a query field, determining a candidate node set based on the query field and the event graph, performing path search in the candidate node set to obtain a candidate event path, and performing time-aware reasoning on the candidate event path based on a preset time chain thinking prompt strategy to obtain a reasoning result; and performing structured analysis, event path alignment and confidence evaluation processing on the reasoning result to obtain an enterprise knowledge base retrieval text with a reference basis, a time label and a confidence label so as to improve the efficiency of retrieving the content of the enterprise knowledge base.

The invention discloses an automatic story generation method and system based on a user portrait, and relates to the technical field of user portrait modeling, and the method comprises the steps: collecting real-time interaction data of a user, generating a candidate keyword set in combination with an RAKE algorithm, calculating a VAD emotion vector, and generating an emotion resonance keyword set based on the candidate keyword set and the VAD emotion vector; generating an event sequence based on an emotional resonance keyword set, constructing an event graph by using the event sequence, optimizing the event sequence through a LaMDA model, calculating an edge weight, updating the event graph, obtaining probability distribution of the updated event graph through VGAE coding, and generating a plot skeleton in combination with a VAD emotional vector; and calculating a gating weight vector, calculating a dynamic user portrait vector based on the gating weight vector, calculating a loss function and updating an encoder-decoder model, and obtaining an optimized story text. The emotional consistency, the plot continuity and the individuation level of the generated stories are improved.

This invention relates to a closed-loop data system and method for embodied intelligent robots. The system includes: a data acquisition module for acquiring multimodal operational data of the embodied intelligent robot during task execution and constructing multiple embodied state nodes based on the multimodal operational data; a construction module for constructing event correlation relationships between embodied state nodes based on temporal and physical constraints, forming an embodied state event graph; an anomaly identification module for identifying anomalous embodied state nodes in the embodied state event graph and marking them as failure source nodes; and a propagation analysis module for determining failure propagation paths forward or backward from the failure source nodes based on event correlation relationships. Using the above scheme, the operational data during the task execution of the embodied intelligent robot can be effectively analyzed, anomalous states can be accurately identified, and data-driven closed-loop optimization can be achieved.

The invention discloses an event identification method, a traffic event identification method and an event identification system, and relates to the field of large model technology and monitoring. The method comprises the following steps: in a process of monitoring at least one moving object in a to-be-monitored area, acquiring an event video in the to-be-monitored area under the condition that occurrence of an event caused by a target moving object is monitored; identifying an event contained in the event video, and determining whether the event video contains the event; when the event video contains the event, intercepting the event video to obtain at least one event image containing the event; and identifying the event contained in the at least one event image by using the multi-modal large model to obtain the event type of the event. The technical problem that the event recognition effect is poor in the related technology is solved.

The present disclosure discloses a processing method and device of an event, a computer device and a storage medium, and the implementation scheme is as follows: a first vector corresponding to a to-be-processed event and a second vector corresponding to each reference event in a preset event graph are determined; each associated event related to the to-be-processed event is determined from the event graph according to the similarity between the first vector and the second vector; each transition probability between the to-be-processed event and each associated event is determined according to the similarity between the first vector and the second vector corresponding to each associated event; and a type label corresponding to the to-be-processed event is determined according to each transition probability and a label vector corresponding to each associated event. Thus, the type label corresponding to the to-be-processed event is determined by combining the semantic information of the to-be-processed event, each transition probability between the to-be-processed event and each associated event, and the label vector corresponding to each associated event, thereby improving the rationality and reliability of the event handling suggestion and reducing the event processing cost.

The invention discloses a security event intelligent understanding method based on semantic segmentation and a knowledge graph, which relates to the technical field of intelligent security and comprises the following steps: processing security video image frames, performing region segmentation on an image by adopting a semantic segmentation model, and extracting mask data of a key region of the image; constructing a target entity set in combination with regional pixel distribution and category labels, and performing boundary position coding and time sequence numbering on entities; the method comprises the following steps: mapping a target entity set into entity nodes in a graph through a security and protection knowledge graph structure, performing edge-type matching on a semantic relationship between the nodes based on an event triggering rule, generating a preliminary event semantic relationship graph, performing graph structure embedding calculation on the entity nodes and relationship edges, and generating a preliminary event semantic relationship graph; and performing semantic reasoning on the event graph in combination with an attention weight mechanism, and outputting an event type and a risk level. According to the method, the detection precision and real-time performance of the security event in a complex scene are improved, and the credibility of the decision is enhanced through an interpretable map structure.

The invention discloses a logistics scheduling management method based on an ant colony algorithm. The method comprises the following steps: constructing a logistics network model constrained by a capacity time window; carrying out ant colony search to generate a scheduling scheme set and carrying out local modeling to obtain a scheduling constraint manifold; converting the scheduling scheme into a vehicle event graph and introducing a time phase to obtain an event phase code; defining a manifold projection operator on the scheduling constraint manifold, mapping an event phase code into a feasible code, and setting a water wave parameter according to a difference value; a water wave model with event phase codes as individuals is constructed, candidate codes are generated through translation, torsion and event subgraph reconnection, and projection is feasible; constructing a cost field and a risk field according to the transportation cost and the time window default risk, and updating event phase codes and ant colony parameters according to a gradient; and when the model converges, outputting a logistics scheduling scheme and generating a vehicle warehouse instruction. Scheduling constraint manifold and water wave optimization are introduced on the basis of ant colony search, and cost and risk are balanced under capacity constraint.

The application provides a multi-machine alarm log association method and device, electronic equipment and a storage medium, and belongs to the technical field of communication. In the method, an internal memory graph is used for association. The internal memory graph can realize multi-machine (between nodes of different entity elements) association. The association points of the internal memory graph are at least one of the sub-nodes, that is, multiple association points are allowed to be set, the dimension of the association is greatly increased, the use of the internal memory graph compresses the internal memory occupation, the merging and multi-machine association are all carried out in the internal memory, the storage and network dependence is removed, and real-time and efficient association can be realized. When the multi-machine association is carried out, the association is combined according to the association point granularity, and the association calculation amount is greatly reduced. In addition, the first internal memory graph and the second internal memory graph can be automatically combined and expanded, the third internal memory graph manually triggered and expanded by the front end can be associated with the current attack event graph, and the expandability is good.

A monitoring apparatus includes a processor, wherein the processor provides a plurality of images to a first area of the user interface, generates an event image and a representative image from an image in which a set event is detected from among the plurality of images, and provides an event window in which the representative image is displayed in a second area, and the event image is automatically reproduced near the image from which the event is detected in the first area.

In order to solve the problem of event prediction reasoning, the invention provides an enhanced path reasoning algorithm based on a fused context script event graph. By analyzing the limitation of an existing event prediction reasoning algorithm, an event prediction reasoning problem is modeled as a Markov decision process (MDP), and a reinforcement learning framework including a state space, an action space, a strategy function, a transfer function and a reward mechanism is constructed. In the aspect of algorithm design, design implementation of the whole algorithm is completed through a graph structure learning module, an action scoring module, a random strategy generation module, a reward calculation module and a strategy optimization module. According to the model, training and test verification are carried out on an MCNC and a self-built building construction accident report data set, the result shows that the model is superior to other existing prediction models in a downstream event prediction reasoning task, the accuracy rate is improved by 2.61% compared with an optimal RoBERTa-RF model, the result has statistical significance, and the method is suitable for popularization and application. According to the method, a new methodology framework is provided for an event prediction task, and a new path is opened up for cognitive calculation research of an event atlas through an interpretable path reasoning mechanism.

This application relates to the field of intelligent construction and engineering management technology, and discloses an automatic generation system for construction site engineering documents based on multimodal information acquisition. The system includes modules for on-site perception and acquisition, data transmission network, cloud-based core processing, and user interaction terminal. Based on a BIM model and construction plan, the system constructs a dynamic causal event graph. Utilizing spatiotemporal semantic anchors and multimodal projection mapping technology, it accurately associates on-site collected images and sensor data with process event nodes. A cloud-based logic verification service module calculates the completeness status of event nodes in real time. When a missing evidence chain is detected, it proactively generates reverse control commands to schedule on-site equipment to complete the data. After the node status is complete, the system automatically extracts data, generates standardized documents, and uploads them to the blockchain for storage. This invention solves the problems of semantic discrepancies in construction data, missing logical verification, and low document reliability, achieving automated closed-loop generation and reliable delivery of engineering documents.

The invention belongs to the field of signaling log analysis, and particularly relates to a dynamic self-adaptive signaling log analysis method based on a large language model, which comprises the following steps: collecting an industrial log and carrying out preprocessing operation; the self-adaptive analyzer converts the preprocessed industrial log into candidate elements and outputs confidence; constructing a physical rule of an industrial site; marking the candidate elements by using physical rules of an industrial site through a consistency calibrator; writing the coincident annotations into the event atlas; trying to deduce missing elements according to adjacent events and physical constraints by a completion engine for the non-conforming standard; judging the confidence degree of the inference result; the system periodically feeds back a verification result and a manually confirmed result to the analyzer for online fine tuning; according to the method, log analysis and credibility judgment can be completed at the edge in cooperation with the cloud end, a high-precision and low-delay dynamic self-adaptive signaling log analysis system can be achieved in a multi-source heterogeneous environment, and the automation degree of anomaly recognition and event reconstruction is remarkably improved.